|Title||Nearest centroid classification on a trapped ion quantum computer|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||S Johri, S Debnath, A Mocherla, A Singk, A Prakash, J Kim, and I Kerenidis|
|Journal||Npj Quantum Information|
Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
|Short Title||Npj Quantum Information|